Download or read book Neural Networks and Sparse Information Acquisition written by Behrooz Kamary Aliabadi and published by . This book was released on 2013 with total page 127 pages. Available in PDF, EPUB and Kindle. Book excerpt: Cette thèse traite de mémoires associatives neuro-inspirées. Une extension des réseaux de neurones récurrents et binaires introduits par Gripon et Berrou a été étudiée pour y accroître les effets de la parcimonie. Dans cette nouvelle version de réseaux de neurones, l'information est portée par des motifs graphiques (cliques) qui ne font appel qu'à une fraction des ressources disponibles. Ces motifs peuvent également être de tailles différentes et donc porter une information de longueur variable. Nous avons validé le concept et calculé les limites de capacité et de correction d'effacements en fonction des taux d'erreurs recherchés. Ces limites ont été comparées à des résultats de simulation obtenus dans différentes situations de test. Nous avons également analysé ces réseaux sous l'éclairage de la théorie de l'information et établi un lien avec la problématique de l'acquisition compressée (compressed sensing).

Download or read book Data Driven Science and Engineering written by Steven L. Brunton and published by Cambridge University Press. This book was released on 2022-05-05 with total page 615 pages. Available in PDF, EPUB and Kindle. Book excerpt: A textbook covering data-science and machine learning methods for modelling and control in engineering and science, with Python and MATLAB®.

Download or read book Soft Computing in Measurement and Information Acquisition written by Leon Reznik and published by Springer. This book was released on 2012-12-06 with total page 297 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume covers the fields of measurement and information acqulSltlon. It contains a collection of papers representing the current research trends in these areas. What are those trends? The first one is the enormous growth in the amount of information and the amazing technologies, which make this information available anywhere and anytime. The second one is a substantial development of methods of the information presentation including, to name just a few, multimedia, virtual environment, computer animation. The third one is the all-time boosting demand for improving the quality of decisions made on the base of this information in various applications ranging from engineering to business. Nowadays information acquisition should not only provide more information but also provide it in such a way as to assure effective and efficient processing of this information. And here comes a relatively new methodology of soft computing. Application of soft computing in measurement and information acquisition is considered in this volume.

Download or read book Experimental Data Acquisition and Modeling of Three dimensional Deformable Objects Using Neural Networks written by Ana-Maria Cretu and published by . This book was released on 2009 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Nowadays there are many technologies and design tools available to accurately obtain and model the geometric shape and the color of objects. However, these methods are not able to provide any information about the elasticity of the objects. This thesis presents a general-purpose scheme for measuring, constructing and representing geometric and elastic behavior of deformable objects without a priori knowledge on the shape and the material that the objects under study are made of. The proposed solution is based on an advantageous combination of neural network architectures and an original force-deformation measurement procedure. An innovative non-uniform selective data acquisition algorithm based on self-organizing neural architectures (namely neural gas and growing neural gas) is developed to selectively and iteratively identify regions of interest and guide the acquisition of data only on those points that are relevant for both the geometric model and the mapping of the elastic behavior, starting from a sparse point-cloud of an object. Multi-resolution object models are obtained using the initial sparse model or the (growing or) neural gas map if a more compressed model is desired, and augmenting it with the higher resolution measurements selectively collected over the regions of interest. A feedforward neural network is then employed to capture the complex relationship between an applied force, its magnitude, its angle of application and its point of interaction, the object pose and the deformation stage of the object on one side, and the object surface deformation for each region with similar geometric and elastic behavior on the other side. The proposed framework works directly from raw range data and obtains compact point-based models. It can deal with different types of materials, distinguishes between the different stages of deformation of an object and models homogeneous and non-homogeneous objects as well. It also offers the desired degree of control to the user.

Download or read book Multifaceted approaches for Data Acquisition Processing Communication written by Chinmay Chakraborty and published by CRC Press. This book was released on 2024-06-24 with total page 293 pages. Available in PDF, EPUB and Kindle. Book excerpt: The objective of the conference is to bring to focus the recent technological advancements across all the stages of data analysis including acquisition, processing, and communication. Advancements in acquisition sensors along with improved storage and computational capabilities, have stimulated the progress in theoretical studies and state-of-the-art real-time applications involving large volumes of data. This compels researchers to investigate the new challenges encountered, where traditional approaches are incapable of dealing with large, complicated new forms of data.

Download or read book Neural Information Processing written by Bao-Liang Lu and published by Springer Science & Business Media. This book was released on 2011-10-26 with total page 810 pages. Available in PDF, EPUB and Kindle. Book excerpt: The three volume set LNCS 7062, LNCS 7063, and LNCS 7064 constitutes the proceedings of the 18th International Conference on Neural Information Processing, ICONIP 2011, held in Shanghai, China, in November 2011. The 262 regular session papers presented were carefully reviewed and selected from numerous submissions. The papers of part I are organized in topical sections on perception, emotion and development, bioinformatics, biologically inspired vision and recognition, bio-medical data analysis, brain signal processing, brain-computer interfaces, brain-like systems, brain-realistic models for learning, memory and embodied cognition, Clifford algebraic neural networks, combining multiple learners, computational advances in bioinformatics, and computational-intelligent human computer interaction. The second volume is structured in topical sections on cybersecurity and data mining workshop, data mining and knowledge doscovery, evolutionary design and optimisation, graphical models, human-originated data analysis and implementation, information retrieval, integrating multiple nature-inspired approaches, kernel methods and support vector machines, and learning and memory. The third volume contains all the contributions connected with multi-agent systems, natural language processing and intelligent Web information processing, neural encoding and decoding, neural network models, neuromorphic hardware and implementations, object recognition, visual perception modelling, and advances in computational intelligence methods based pattern recognition.

Download or read book Multivariate Statistical Machine Learning Methods for Genomic Prediction written by Osval Antonio Montesinos López and published by Springer Nature. This book was released on 2022-02-14 with total page 707 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is open access under a CC BY 4.0 license This open access book brings together the latest genome base prediction models currently being used by statisticians, breeders and data scientists. It provides an accessible way to understand the theory behind each statistical learning tool, the required pre-processing, the basics of model building, how to train statistical learning methods, the basic R scripts needed to implement each statistical learning tool, and the output of each tool. To do so, for each tool the book provides background theory, some elements of the R statistical software for its implementation, the conceptual underpinnings, and at least two illustrative examples with data from real-world genomic selection experiments. Lastly, worked-out examples help readers check their own comprehension.The book will greatly appeal to readers in plant (and animal) breeding, geneticists and statisticians, as it provides in a very accessible way the necessary theory, the appropriate R code, and illustrative examples for a complete understanding of each statistical learning tool. In addition, it weighs the advantages and disadvantages of each tool.

Download or read book Neural Networks and Statistical Learning written by Ke-Lin Du and published by Springer Nature. This book was released on 2019-09-12 with total page 988 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a broad yet detailed introduction to neural networks and machine learning in a statistical framework. A single, comprehensive resource for study and further research, it explores the major popular neural network models and statistical learning approaches with examples and exercises and allows readers to gain a practical working understanding of the content. This updated new edition presents recently published results and includes six new chapters that correspond to the recent advances in computational learning theory, sparse coding, deep learning, big data and cloud computing. Each chapter features state-of-the-art descriptions and significant research findings. The topics covered include: • multilayer perceptron; • the Hopfield network; • associative memory models;• clustering models and algorithms; • t he radial basis function network; • recurrent neural networks; • nonnegative matrix factorization; • independent component analysis; •probabilistic and Bayesian networks; and • fuzzy sets and logic. Focusing on the prominent accomplishments and their practical aspects, this book provides academic and technical staff, as well as graduate students and researchers with a solid foundation and comprehensive reference on the fields of neural networks, pattern recognition, signal processing, and machine learning.

Download or read book Neuro inspired Architectures for the Acquisition and Processing of Visual Information written by Ala Aboudib and published by . This book was released on 2016 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computer vision and machine learning are two hot research topics that have witnessed major breakthroughs in recent years. Much of the advances in these domains have been the fruits of many years of research on the visual cortex and brain function. In this thesis, we focus on designing neuro-inspired architectures for processing information along three different stages of the visual cortex. At the lowest stage, we propose a neural model for the acquisition of visual signals. This model is adapted to emulating eye movements and is closely inspired by the function and the architecture of the retina and early layers of the ventral stream. On the highest stage, we address the memory problem. We focus on an existing neuro-inspired associative memory model called the Sparse Clustered Network. We propose a new information retrieval algorithm that offers more flexibility and a better performance over existing ones. Furthermore, we suggest a generic formulation within which all existing retrieval algorithms can fit. It can also be used to guide the design of new retrieval approaches in a modular fashion. On the intermediate stage, we propose a new way for dealing with the image feature correspondence problem using a neural network model. This model deploys the structure of Sparse Clustered Networks, and offers a gain in matching performance over state-of-the-art, and provides a useful insight on how neuro-inspired architectures can serve as a substrate for implementing various vision tasks.

Download or read book Information Theory Inference and Learning Algorithms written by David J. C. MacKay and published by Cambridge University Press. This book was released on 2003-09-25 with total page 694 pages. Available in PDF, EPUB and Kindle. Book excerpt: Information theory and inference, taught together in this exciting textbook, lie at the heart of many important areas of modern technology - communication, signal processing, data mining, machine learning, pattern recognition, computational neuroscience, bioinformatics and cryptography. The book introduces theory in tandem with applications. Information theory is taught alongside practical communication systems such as arithmetic coding for data compression and sparse-graph codes for error-correction. Inference techniques, including message-passing algorithms, Monte Carlo methods and variational approximations, are developed alongside applications to clustering, convolutional codes, independent component analysis, and neural networks. Uniquely, the book covers state-of-the-art error-correcting codes, including low-density-parity-check codes, turbo codes, and digital fountain codes - the twenty-first-century standards for satellite communications, disk drives, and data broadcast. Richly illustrated, filled with worked examples and over 400 exercises, some with detailed solutions, the book is ideal for self-learning, and for undergraduate or graduate courses. It also provides an unparalleled entry point for professionals in areas as diverse as computational biology, financial engineering and machine learning.

Download or read book Proceedings of the International Conference on Intelligent Vision and Computing ICIVC 2021 written by Harish Sharma and published by Springer Nature. This book was released on 2022-03-23 with total page 592 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book gathers outstanding research papers presented at the International Conference on Intelligent Vision and Computing (ICIVC 2021), held online during October 03–04, 2021. ICIVC 2021 is organised by Sur University, Oman. The book presents novel contributions in intelligent vision and computing and serves as reference material for beginners and advanced research. The topics covered are intelligent systems, intelligent data analytics and computing, intelligent vision and applications collective intelligence, soft computing, optimization, cloud computing, machine learning, intelligent software, robotics, data science, data security, big data analytics, and signal natural language processing.

Download or read book Knowledge Acquisition with Neural Networks written by A. Ioannides and published by . This book was released on 1990 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Advances in Neural Information Processing Systems 8 written by David S. Touretzky and published by MIT Press. This book was released on 1996 with total page 1128 pages. Available in PDF, EPUB and Kindle. Book excerpt: The past decade has seen greatly increased interaction between theoretical work in neuroscience, cognitive science and information processing, and experimental work requiring sophisticated computational modeling. The 152 contributions in NIPS 8 focus on a wide variety of algorithms and architectures for both supervised and unsupervised learning. They are divided into nine parts: Cognitive Science, Neuroscience, Theory, Algorithms and Architectures, Implementations, Speech and Signal Processing, Vision, Applications, and Control. Chapters describe how neuroscientists and cognitive scientists use computational models of neural systems to test hypotheses and generate predictions to guide their work. This work includes models of how networks in the owl brainstem could be trained for complex localization function, how cellular activity may underlie rat navigation, how cholinergic modulation may regulate cortical reorganization, and how damage to parietal cortex may result in neglect. Additional work concerns development of theoretical techniques important for understanding the dynamics of neural systems, including formation of cortical maps, analysis of recurrent networks, and analysis of self- supervised learning. Chapters also describe how engineers and computer scientists have approached problems of pattern recognition or speech recognition using computational architectures inspired by the interaction of populations of neurons within the brain. Examples are new neural network models that have been applied to classical problems, including handwritten character recognition and object recognition, and exciting new work that focuses on building electronic hardware modeled after neural systems. A Bradford Book

Download or read book Neural Information Processing Models and Applications written by Kevin K.W. Wong and published by Springer. This book was released on 2010-11-18 with total page 763 pages. Available in PDF, EPUB and Kindle. Book excerpt: The two volume set LNCS 6443 and LNCS 6444 constitutes the proceedings of the 17th International Conference on Neural Information Processing, ICONIP 2010, held in Sydney, Australia, in November 2010. The 146 regular session papers presented were carefully reviewed and selected from 470 submissions. The papers of part I are organized in topical sections on neurodynamics, computational neuroscience and cognitive science, data and text processing, adaptive algorithms, bio-inspired algorithms, and hierarchical methods. The second volume is structured in topical sections on brain computer interface, kernel methods, computational advance in bioinformatics, self-organizing maps and their applications, machine learning applications to image analysis, and applications.

Download or read book Comprehensive Chemometrics written by Steven Brown and published by Elsevier. This book was released on 2020-05-26 with total page 2948 pages. Available in PDF, EPUB and Kindle. Book excerpt: Comprehensive Chemometrics, Second Edition, Four Volume Set features expanded and updated coverage, along with new content that covers advances in the field since the previous edition published in 2009. Subject of note include updates in the fields of multidimensional and megavariate data analysis, omics data analysis, big chemical and biochemical data analysis, data fusion and sparse methods. The book follows a similar structure to the previous edition, using the same section titles to frame articles. Many chapters from the previous edition are updated, but there are also many new chapters on the latest developments. Presents integrated reviews of each chemical and biological method, examining their merits and limitations through practical examples and extensive visuals Bridges a gap in knowledge, covering developments in the field since the first edition published in 2009 Meticulously organized, with articles split into 4 sections and 12 sub-sections on key topics to allow students, researchers and professionals to find relevant information quickly and easily Written by academics and practitioners from various fields and regions to ensure that the knowledge within is easily understood and applicable to a large audience Presents integrated reviews of each chemical and biological method, examining their merits and limitations through practical examples and extensive visuals Bridges a gap in knowledge, covering developments in the field since the first edition published in 2009 Meticulously organized, with articles split into 4 sections and 12 sub-sections on key topics to allow students, researchers and professionals to find relevant information quickly and easily Written by academics and practitioners from various fields and regions to ensure that the knowledge within is easily understood and applicable to a large audience

Download or read book Machine Learning for Medical Image Reconstruction written by Florian Knoll and published by Springer. This book was released on 2018-09-11 with total page 161 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the First International Workshop on Machine Learning for Medical Reconstruction, MLMIR 2018, held in conjunction with MICCAI 2018, in Granada, Spain, in September 2018. The 17 full papers presented were carefully reviewed and selected from 21 submissions. The papers are organized in the following topical sections: deep learning for magnetic resonance imaging; deep learning for computed tomography, and deep learning for general image reconstruction.

Download or read book Proceedings of the Twenty fourth Annual Conference of the Cognitive Science Society written by Wayne D. Gray and published by Routledge. This book was released on 2019-04-24 with total page 312 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume features the complete text of the material presented at the Twenty-Fourth Annual Conference of the Cognitive Science Society. As in previous years, the symposium included an interesting mixture of papers on many topics from researchers with diverse backgrounds and different goals, presenting a multifaceted view of cognitive science. The volume includes all papers, posters, and summaries of symposia presented at this leading conference that brings cognitive scientists together. The 2002 meeting dealt with issues of representing and modeling cognitive processes as they appeal to scholars in all subdisciplines that comprise cognitive science: psychology, computer science, neuroscience, linguistics, and philosophy.